Spectrum sensing in full-duplex OFDM systems using one-shot learning

Deep learning (DL) has been envisioned as a plausible solution to spectrum sensing, demonstrating an influential role in dynamic spectrum access. Despite their effectiveness, existing DL based sensing methods are heavily environment-sensitive. In other words, the sensing model trained in one environment usually cannot be applied to another, and a large number of labeled samples from the new environment are required to re-train DL architectures. To address the above challenge, we propose a novel approach leveraging the matching network (MN) for environment-robust spectrum sensing (MN-ERSS). Specifically, to improve the quality of input signals of MN, we propose to use a cross-correlation feature of the cyclic prefix (CP) of orthogonal frequency division multiplexing (OFDM) signals as the input data. Then, we propose to employ an advanced technique of one-shot learning, i.e., MN, to automatically extract inherent features from input signals. Moreover, we propose a tailored training strategy to better utilize the data set from the previous environment. The proposed training strategy can accomplish a successful spectrum sensing with the data set from only one previous environment and one sample from the new/testing environment. To the best of our knowledge, this is the first to investigate the environment-robust spectrum sensing by exploring one-shot learning. Extensive simulation results demonstrate that the proposed MN-ERSS significantly outperforms state-of-the-art sensing approaches, i.e., achieving a higher sensing accuracy with only one sample from the testing environment and the data set from one previous environment.